Trustworthy autonomic architecture (TAArch): Implementation and empirical investigation

This paper presents a new architecture for trustworthy autonomic systems. This trustworthy autonomic architecture is different from the traditional autonomic computing architecture and includes mechanisms and instrumentation to explicitly support run-time self-validation and trustworthiness. The state of practice does not lend itself robustly enough to support trustworthiness and system dependability. For example, despite validating system's decisions within a logical boundary set for the system, there’s the possibility of overall erratic behaviour or inconsistency in the system emerging for example, at a different logical level or on a different time scale. So a more thorough and holistic approach, with a higher level of check, is required to convincingly address the dependability and trustworthy concerns. Validation alone does not always guarantee trustworthiness as each individual decision could be correct (validated) but overall system may not be consistent and thus not dependable. A robust approach requires that validation and trustworthiness are designed in and integral at the architectural level, and not treated as add-ons as they cannot be reliably retro-fitted to systems. This paper analyses the current state of practice in autonomic architecture, presents a different architectural approach for trustworthy autonomic systems, and uses a datacentre scenario as the basis for empirical analysis of behaviour and performance. Results show that the proposed trustworthy autonomic architecture has significant performance improvement over existing architectures and can be relied upon to operate (or manage) almost all level of datacentre scale and complexity

Cloud-computing strategies for sustainable ICT utilization : a decision-making framework for non-expert Smart Building managers

Virtualization of processing power, storage, and networking applications via cloud-computing allows Smart Buildings to operate heavy demand computing resources off-premises. While this approach reduces in-house costs and energy use, recent case-studies have highlighted complexities in decision-making processes associated with implementing the concept of cloud-computing. This complexity is due to the rapid evolution of these technologies without standardization of approach by those organizations offering cloud-computing provision as a commercial concern. This study defines the term Smart Building as an ICT environment where a degree of system integration is accomplished. Non-expert managers are highlighted as key users of the outcomes from this project given the diverse nature of Smart Buildings’ operational objectives. This research evaluates different ICT management methods to effectively support decisions made by non-expert clients to deploy different models of cloud-computing services in their Smart Buildings ICT environments. The objective of this study is to reduce the need for costly 3rd party ICT consultancy providers, so non-experts can focus more on their Smart Buildings’ core competencies rather than the complex, expensive, and energy consuming processes of ICT management. The gap identified by this research represents vulnerability for non-expert managers to make effective decisions regarding cloud-computing cost estimation, deployment assessment, associated power consumption, and management flexibility in their Smart Buildings ICT environments. The project analyses cloud-computing decision-making concepts with reference to different Smart Building ICT attributes. In particular, it focuses on a structured programme of data collection which is achieved through semi-structured interviews, cost simulations and risk-analysis surveys. The main output is a theoretical management framework for non-expert decision-makers across variously-operated Smart Buildings. Furthermore, a decision-support tool is designed to enable non-expert managers to identify the extent of virtualization potential by evaluating different implementation options. This is presented to correlate with contract limitations, security challenges, system integration levels, sustainability, and long-term costs. These requirements are explored in contrast to cloud demand changes observed across specified periods. Dependencies were identified to greatly vary depending on numerous organizational aspects such as performance, size, and workload. The study argues that constructing long-term, sustainable, and cost-efficient strategies for any cloud deployment, depends on the thorough identification of required services off and on-premises. It points out that most of today’s heavy-burdened Smart Buildings are outsourcing these services to costly independent suppliers, which causes unnecessary management complexities, additional cost, and system incompatibility. The main conclusions argue that cloud-computing cost can differ depending on the Smart Building attributes and ICT requirements, and although in most cases cloud services are more convenient and cost effective at the early stages of the deployment and migration process, it can become costly in the future if not planned carefully using cost estimation service patterns. The results of the study can be exploited to enhance core competencies within Smart Buildings in order to maximize growth and attract new business opportunities

Cloud engineering is search based software engineering too

Many of the problems posed by the migration of computation to cloud platforms can be formulated and solved using techniques associated with Search Based Software Engineering (SBSE). Much of cloud software engineering involves problems of optimisation: performance, allocation, assignment and the dynamic balancing of resources to achieve pragmatic trade-offs between many competing technical and business objectives. SBSE is concerned with the application of computational search and optimisation to solve precisely these kinds of software engineering challenges. Interest in both cloud computing and SBSE has grown rapidly in the past five years, yet there has been little work on SBSE as a means of addressing cloud computing challenges. Like many computationally demanding activities, SBSE has the potential to benefit from the cloud; ‘SBSE in the cloud’. However, this paper focuses, instead, of the ways in which SBSE can benefit cloud computing. It thus develops the theme of ‘SBSE for the cloud’, formulating cloud computing challenges in ways that can be addressed using SBSE

Cognitive-Aware Network Virtualization Hypervisor for Efficient Resource Provisioning in Software Defined Cloud Networks

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonIntegration of different technologies forms an integral part of modern network engineering and 5G technology deployment. Although Software Defined Networking (SDN) and Network Functions Virtualization (NFV) function well independently, integrating these two technologies present the cooperate advantages to service providers and service users. Operations of cloud computing technologies have been enhanced with the advent of SDN and NFV for efficient solutions deployment and infrastructure management in Software Defined Cloud Datacentre Networks (SDCDCN) where dynamic controllability is indispensable for elastic service provision. The provisioning of joint compute and network resources enabled by SDCN is essential to enforce reasonable Service Level Agreements (SLAs) stating the Quality of Service (QoS) while saving energy consumption and resource wastage. This thesis presents a Cognitive- Aware Network virtualization Hypervisor which was developed from merging the programmable dynamic network control attributes of SDN and the network slicing attributes of NFV to provision joint compute and network resources in SDCDCN for QoS fulfilment and energy efficiency. It focuses on the techniques for allocating Virtual Network Requests on physical hosts and switches considering SLA, QoS, and energy efficiency aspects. The thesis advances the state-of the-art with the following key contributions: A modelling and simulation environment for Software Defined Cloud Datacentre Networks abstracting functionalities and behaviours of virtual and physical network resources. The second is a novel dynamic overbooking algorithm for energy efficiency and SLA enforcement with the migration of virtual machines and network flows. Finally, a performance-aware intelligent overbooking for predicting network resource usage and performance for the next defined time interval considering multiple performance indexes

SHARING WITH LIVE MIGRATION ENERGY OPTIMIZATION TASK SCHEDULER FOR CLOUD COMPUTING DATACENTRES

The use of cloud computing is expanding, and it is becoming the driver for innovation in all companies to serve their customers around the world. A big attention was drawn to the huge energy that was consumed within those datacentres recently neglecting the energy consumption in the rest of the cloud components. Therefore, the energy consumption should be reduced to minimize performance losses, achieve the target battery lifetime, satisfy performance requirements, minimize power consumption, minimize the CO2 emissions, maximize the profit, and maximize resource utilization. Reducing power consumption in the cloud computing datacentres can be achieved by many ways such as managing or utilizing the resources, controlling redundancy, relocating datacentres, improvement of applications or dynamic voltage and frequency scaling. One of the most efficient ways to reduce power is to use a scheduling technique that will find the best task execution order based on the users demands and with the minimum execution time and cloud resources. It is quite a challenge in cloud environment to design an effective and an efficient task scheduling technique which is done based on the user requirements. The scheduling process is not an easy task because within the datacentre there is dissimilar hardware with different capacities and, to improve the resource utilization, an efficient scheduling algorithm must be applied on the incoming tasks to achieve efficient computing resource allocating and power optimization. The scheduler must maintain the balance between the Quality of Service and fairness among the jobs so that the efficiency may be increased. The aim of this project is to propose a novel method for optimizing energy usage in cloud computing environments that satisfy the Quality of Service (QoS) and the regulations of the Service Level Agreement (SLA). Applying a power- and resource-optimised scheduling algorithm will assist to control and improve the process of mapping between the datacentre servers and the incoming tasks and achieve the optimal deployment of the data centre resources to achieve good computing efficiency, network load minimization and reducing the energy consumption in the datacentre. This thesis explores cloud computing energy aware datacentre structures with diverse scheduling heuristics and propose a novel job scheduling technique with sharing and live migration based on file locality (SLM) aiming to maximize efficiency and save power consumed in the datacentre due to bandwidth usage utilization, minimizing the processing time and the system total make span. The propose SLM energy efficient scheduling strategy have four basic algorithms: 1) Job Classifier, 2) SLM job scheduler, 3) Dual fold VM virtualization and 4) VM threshold margins and consolidation. The SLM job classifier worked on categorising the incoming set of user requests to the datacentre in to two different queues based on these requests type and the source file needed to process them. The processing time of each job fluctuate based on the job type and the number of instructions for each job. The second algorithm, which is the SLM scheduler algorithm, dispatch jobs from both queues according to job arrival time and control the allocation process to the most appropriate and available VM based on job similarity according to a predefined synchronized job characteristic table (SJC). The SLM scheduler uses a replicated host’s infrastructure to save the wasted idle hosts energy by maximizing the basic host’s utilization as long as the system can deal with workflow while setting replicated hosts on off mode. The third SLM algorithm, the dual fold VM algorithm, divide the active VMs in to a top and low level slots to allocate similar jobs concurrently which maximize the host utilization at high workload and reduce the total make span. The VM threshold margins and consolidation algorithm set an upper and lower threshold margin as a trigger for VMs consolidation and load balancing process among running VMs, and deploy a continuous provisioning of overload and underutilize VMs detection scheme to maintain and control the system workload balance. The consolidation and load balancing is achieved by performing a series of dynamic live migrations which provides auto-scaling for the servers with in the datacentres. This thesis begins with cloud computing overview then preview the conceptual cloud resources management strategies with classification of scheduling heuristics. Following this, a Competitive analysis of energy efficient scheduling algorithms and related work is presented. The novel SLM algorithm is proposed and evaluated using the CloudSim toolkit under number of scenarios, then the result compared to Particle Swarm Optimization algorithm (PSO) and Ant Colony Algorithm (ACO) shows a significant improvement in the energy usage readings levels and total make span time which is the total time needed to finish processing all the tasks

A prescriptive analytics approach for energy efficiency in datacentres.

Given the evolution of Cloud Computing in recent years, users and clients adopting Cloud Computing for both personal and business needs have increased at an unprecedented scale. This has naturally led to the increased deployments and implementations of Cloud datacentres across the globe. As a consequence of this increasing adoption of Cloud Computing, Cloud datacentres are witnessed to be massive energy consumers and environmental polluters. Whilst the energy implications of Cloud datacentres are being addressed from various research perspectives, predicting the future trend and behaviours of workloads at the datacentres thereby reducing the active server resources is one particular dimension of green computing gaining the interests of researchers and Cloud providers. However, this includes various practical and analytical challenges imposed by the increased dynamism of Cloud systems. The behavioural characteristics of Cloud workloads and users are still not perfectly clear which restrains the reliability of the prediction accuracy of existing research works in this context. To this end, this thesis presents a comprehensive descriptive analytics of Cloud workload and user behaviours, uncovering the cause and energy related implications of Cloud Computing. Furthermore, the characteristics of Cloud workloads and users including latency levels, job heterogeneity, user dynamicity, straggling task behaviours, energy implications of stragglers, job execution and termination patterns and the inherent periodicity among Cloud workload and user behaviours have been empirically presented. Driven by descriptive analytics, a novel user behaviour forecasting framework has been developed, aimed at a tri-fold forecast of user behaviours including the session duration of users, anticipated number of submissions and the arrival trend of the incoming workloads. Furthermore, a novel resource optimisation framework has been proposed to avail the most optimum level of resources for executing jobs with reduced server energy expenditures and job terminations. This optimisation framework encompasses a resource estimation module to predict the anticipated resource consumption level for the arrived jobs and a classification module to classify tasks based on their resource intensiveness. Both the proposed frameworks have been verified theoretically and tested experimentally based on Google Cloud trace logs. Experimental analysis demonstrates the effectiveness of the proposed framework in terms of the achieved reliability of the forecast results and in reducing the server energy expenditures spent towards executing jobs at the datacentres.N/

An Energy-Efficient Multi-Cloud Service Broker for Green Cloud Computing Environment

The heavy demands on cloud computing resources have led to a substantial growth in energy consumption of the data transferred between cloud computing parties (i.e., providers, datacentres, users, and services) and in datacentre’s services due to the increasing loads on these services. From one hand, routing and transferring large amounts of data into a datacentre located far from the user’s geographical location consume more energy than just processing and storing the same data on the cloud datacentre. On the other hand, when a cloud user submits a job (in the form of a set of functional and non-functional requirements) to a cloud service provider (aka, datacentre) via a cloud services broker; the broker becomes responsible to find the best-fit service to the user request based mainly on the user’s requirements and Quality of Service (QoS) (i.e., response time, latency). Hence, it becomes a high necessity to locate the lowest energy consumption route between the user and the designated datacentre; and the minimum possible number of most energy efficient services that satisfy the user request. In fact, finding the most energy-efficient route to the datacentre, and most energy efficient service(s) to the user are the biggest challenges of multi-cloud broker’s environment. This thesis presents and evaluates a novel multi-cloud broker solution that contains three innovative models and their associated algorithms. The first one is aimed at finding the most energy efficient route, among multiple possible routes, between the user and cloud datacentre. The second model is to find and provide the lowest possible number of most energy efficient services in order to minimise data exchange based on a bin-packing approach. The third model creates an energy-aware composition plan by integrating the most energy efficient services, in order to fulfil user requirements. The results demonstrated a favourable performance of these models in terms of selecting the most energy efficient route and reaching the least possible number of services for an optimum and energy efficient composition

Operating policies for energy efficient large scale computing

PhD ThesisEnergy costs now dominate IT infrastructure total cost of ownership, with datacentre operators predicted to spend more on energy than hardware infrastructure in the next five years. With Western European datacentre power consumption estimated at 56 TWh/year in 2007 and projected to double by 2020, improvements in energy efficiency of IT operations is imperative. The issue is further compounded by social and political factors and strict environmental legislation governing organisations. One such example of large IT systems includes high-throughput cycle stealing distributed systems such as HTCondor and BOINC, which allow organisations to leverage spare capacity on existing infrastructure to undertake valuable computation. As a consequence of increased scrutiny of the energy impact of these systems, aggressive power management policies are often employed to reduce the energy impact of institutional clusters, but in doing so these policies severely restrict the computational resources available for high-throughput systems. These policies are often configured to quickly transition servers and end-user cluster machines into low power states after only short idle periods, further compounding the issue of reliability. In this thesis, we evaluate operating policies for energy efficiency in large-scale computing environments by means of trace-driven discrete event simulation, leveraging real-world workload traces collected within Newcastle University. The major contributions of this thesis are as follows: i) Evaluation of novel energy efficient management policies for a decentralised peer-to-peer (P2P) BitTorrent environment. ii) Introduce a novel simulation environment for the evaluation of energy efficiency of large scale high-throughput computing systems, and propose a generalisable model of energy consumption in high-throughput computing systems. iii iii) Proposal and evaluation of resource allocation strategies for energy consumption in high-throughput computing systems for a real workload. iv) Proposal and evaluation for a realworkload ofmechanisms to reduce wasted task execution within high-throughput computing systems to reduce energy consumption. v) Evaluation of the impact of fault tolerance mechanisms on energy consumption

Holistic and Energy-Efficient Management of Datacentres

The overall power consumption of datacentres is increasing tremendously due to the high demand of digital services. Moreover, the cooling load contributes up to 50% of the power consumption due to the higher densities of newer versions of servers. However, there is an increased awareness in the operations of the sub-systems, i.e. workload, cooling load and power consumption. This awareness of the interactions between the sub-systems provides a better understanding for maintaining the datacentre as an energy-efficient infrastructure. A direct contact liquid cooling technology is examined extensively by retrofitting to an air-cooled server. First the conventional SunFire V20z air-cooled server is benchmarked via SPECpower_ssj2008 workload to obtain some standard values. The server is placed inside a wind tunnel to ensure a controllable environment. Then an overall evaluation of the retrofitted server is presented and compared with the standard server. The retrofitted server shows a reduced cooling power consumption of 29%. In addition, the performance to power ratio increases by 10% comparing to the conventional server. The liquid cooling technology keeps the central processes units (CPUs) up to 10 oC colder than the air-cooled server. Furthermore, the new server operates in an 88% lower noise after the replacement of four fans by two pumps. However, the main restriction of using such a solution is the risk of bringing water into the microelectronics due to leakage and condensation of water. A fully immersed encapsulated server is then investigated to assess the validity of simulating the immersed server as a porous layer. This simulation uses Darcy flow with mass, momentum and energy conservation equations. The model shows a quantitive and qualitative accuracy compared to the previous work. The model shows that the distance between processors has a strong effect on the thermal behaviour of the encapsulated server by 13.3% compared to servers’ dimensions. Moreover, the model presents the optimal design and geometry of an encapsulated server with respect to the thermal performance. Although the model is simple, it can be used for an initial prediction of the server design. This is due to the limitation of capturing the thermal behaviour of a full model. A holistic power consumption model is presented to capture the interactive relationships between servers’ sub-system. The power model relies on experimental work and is constructed based on the collected data from different cooling configurations. The model captures a detailed breakdown of the power consumption and therefore presents an accurate calculation of the partial power usage effectiveness metric. The results are limited to one microelectronic architecture within a specific IT load type. However, the results show that reducing the cooling load by 7% and increasing the performance by 5% leads to lower the partial power usage effectiveness by 1%. Finally, the current study explores the usage of an evaporative air handling unit for energy-efficient datacentres. The air handling unit is capable of run dry and wet cooling operation. The cooling system operated successfully during July and August 2016, in Leeds. The wet cooling has a higher thermal performance than the dry cooler due to the large heat capacity of water compared to air. Therefore, the wet cooling configuration records a power usage effectiveness lower than the dry cooling by about 6.4%

Data Replication and Its Alignment with Fault Management in the Cloud Environment

Nowadays, the exponential data growth becomes one of the major challenges all over the world. It may cause a series of negative impacts such as network overloading, high system complexity, and inadequate data security, etc. Cloud computing is developed to construct a novel paradigm to alleviate massive data processing challenges with its on-demand services and distributed architecture. Data replication has been proposed to strategically distribute the data access load to multiple cloud data centres by creating multiple data copies at multiple cloud data centres. A replica-applied cloud environment not only achieves a decrease in response time, an increase in data availability, and more balanced resource load but also protects the cloud environment against the upcoming faults. The reactive fault tolerance strategy is also required to handle the faults when the faults already occurred. As a result, the data replication strategies should be aligned with the reactive fault tolerance strategies to achieve a complete management chain in the cloud environment. In this thesis, a data replication and fault management framework is proposed to establish a decentralised overarching management to the cloud environment. Three data replication strategies are firstly proposed based on this framework. A replica creation strategy is proposed to reduce the total cost by jointly considering the data dependency and the access frequency in the replica creation decision making process. Besides, a cloud map oriented and cost efficiency driven replica creation strategy is proposed to achieve the optimal cost reduction per replica in the cloud environment. The local data relationship and the remote data relationship are further analysed by creating two novel data dependency types, Within-DataCentre Data Dependency and Between-DataCentre Data Dependency, according to the data location. Furthermore, a network performance based replica selection strategy is proposed to avoid potential network overloading problems and to increase the number of concurrent-running instances at the same time